After a few months of dealing with cloud GPUs, I decided to get a used 12GB VRAM GPU for my desktop. Hopefully I’ll be able to train at least 1 useful model on it.


There are several tools for fine-tuning models. A popular tool is Axolotl. It’s obviously more complicated than using OpenAI’s platform, but it’s free and you can customize it to your heart’s content. When fine-tuning, having more VRAM is always better. I’ve found that 12GB is the minimum for training, but 24GB is certainly ideal. Even then, I imagine 24GB will be limiting even still. At this point, I’ve accepted that for large tasks, I’ll need to use cloud GPUs.


When limited on VRAM, I’d recommend looking at Unsloth’s fine-tuning notebook. It’s requires less VRAM for the same results in most cases.

Use PyTorch Docker Images

During the first few tries, I was install pip packages to my local machine. This was a mistake. There are so many versioning issues that can arise. Instead, I recommend using the official PyTorch Docker images with:

docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest

and doing all of your work within the container. This is the same environment you’ll use when renting a cloud GPU, so it doesn’t hurt to get comfortable with it.

Fine-Tuning with Axolotl (WIP)

  1. Prepare your dataset in a standard format (alpaca, etc.)
  2. Specify your base model, model type and tokenizer type
  3. Run the fine-tuning command